Method to prepare virtual assay using fourier transform ion cyclotron resonance mass spectroscopy

ABSTRACT

Systems and methods are disclosed for providing virtual assays of an oil sample such as crude oil based on Fourier transform ion cyclotron resonance (FT-ICR) mass spectroscopy carried out on the oil sample, and the density of the oil sample. The virtual assay provides a full range of information about fractions of the oil sample including naphtha, gas oil, vacuum gas oil, vacuum residue, and other information about the properties of the oil sample. Using the system and method herein, the virtual assay data pertaining to these several fractions of the oil sample and the oil sample itself are obtained without fractionation of the oil sample into the several components.

RELATED APPLICATIONS

Not applicable.

BACKGROUND Field of the Invention

The present invention relates to methods and systems for evaluating an oil sample such as crude oil to provide a virtual assay.

Description of Related Art

Crude oil originates from the decomposition and transformation of aquatic, mainly marine, living organisms and/or land plants that became buried under successive layers of mud and silt some 15-500 million years ago. They are essentially very complex mixtures of many thousands of different hydrocarbons. In addition to crude oils varying from one geographical region to another and from field to field, it has also been observed that the properties of the crude oil from one field may change with time, as oil is withdrawn from different levels or areas of the field. Depending on the source and/or time of withdrawal, the oil predominantly contains various proportions of straight and branched-chain paraffins, cycloparaffins, and naphthenic, aromatic, and polynuclear aromatic hydrocarbons. These hydrocarbons can be gaseous, liquid, or solid under normal conditions of temperature and pressure, depending on the number and arrangement of carbon atoms in the molecules.

Crude oils vary widely in their physical and chemical properties from one geographical region to another and from field to field. Crude oils are usually classified into three groups according to the nature of the hydrocarbons they contain: paraffinic, naphthenic, asphaltic, and their mixtures. The differences are due to the different proportions of the various molecular types and sizes. One crude oil can contain mostly paraffins, another mostly naphthenes. Whether paraffinic or naphthenic, one can contain a large quantity of lighter hydrocarbons and be mobile or contain dissolved gases; another can consist mainly of heavier hydrocarbons and be highly viscous, with little or no dissolved gas. Crude oils can also include heteroatoms containing sulfur, nitrogen, nickel, vanadium and other elements in quantities that impact the refinery processing of the crude oil fractions. Light crude oils or condensates can contain sulfur in concentrations as low as 0.01 W %; in contrast, heavy crude oils can contain as much as 5-6 W %. Similarly, the nitrogen content of crude oils can range from 0.001-1.0 W %.

The nature of the crude oil governs, to a certain extent, the nature of the products that can be manufactured from it and their suitability for special applications. A naphthenic crude oil will be more suitable for the production of asphaltic bitumen, a paraffinic crude oil for wax. A naphthenic crude oil, and even more so an aromatic one, will yield lubricating oils with viscosities that are sensitive to temperature. However, with modern refining methods there is greater flexibility in the use of various crude oils to produce many desired type of products.

A crude oil assay is a traditional method of determining the nature of crude oils for benchmarking purposes. Crude oils are subjected to true boiling point (TBP) distillations and fractionations to provide different boiling point fractions. The crude oil distillations are carried out using the American Standard Testing Association (ASTM) Method D 2892. Common fractions and their corresponding nominal boiling points or boiling point ranges are given in Table 1.

The yields, composition, physical and indicative properties of these crude oil fractions, where applicable, are then determined during the crude assay work-up calculations. Typical compositional and property information obtained from a crude oil assay is given in Table 2.

Due to the number of distillation cuts and the number of analyses involved, the crude oil assay work-up is both costly and time consuming. In a typical refinery, crude oil is first fractionated in the atmospheric distillation column to separate sour gas and light hydrocarbons, including methane, ethane, propane, butanes and hydrogen sulfide, naphtha (for instance having a nominal boiling point range of about 36-180° C.), kerosene (for instance having a nominal boiling point range of about 180-240° C.), gas oil (for instance having a nominal boiling point range of about 240-370° C.) and atmospheric residue (for instance having a nominal boiling point range of about >370° C.). The atmospheric residue from the atmospheric distillation column is either used as fuel oil or sent to a vacuum distillation unit, depending on the configuration of the refinery. The principal products obtained from vacuum distillation are vacuum gas oil (for instance having a nominal boiling point range of about 370-520° C.) and vacuum residue (for instance having a nominal boiling point range of about >520° C.). Crude assay data is conventionally obtained from individual analysis of these cuts, separately for each type of data sought for the assay (that is, elemental composition, physical property and indicative property), to help refiners to understand the general composition of the crude oil fractions and properties so that the fractions can be processed most efficiently and effectively in an appropriate refining unit. Indicative properties are used to determine the engine/fuel performance or usability or flow characteristic or composition.

Whole Crude Oil Properties

Many properties are routinely measured for crudes. Some of the most common factors affecting crude oil handling, processing, and value include the following: density; viscosity; pour point; Reid vapor pressure (RVP); carbon residue; sulfur; nitrogen; metals; salt content; hydrogen sulfide; Total Acidity Number (TAN). These are described in more detail below:

-   -   Density, measured for example by the ASTM D287 method, is the         weight of a substance for a given unit of volume. Density of         crude oil or crude products is measured as specific gravity         comparing the density of the crude or product to the density of         water (usually expressed as gm/cc) or API gravity (° API or         degrees API).     -   Viscosity, measured for example by the ASTM D 445 method, is the         measure of the resistance of a liquid to flow, thereby         indicating the pumpability of the oil. Kinematic viscosity is         the viscosity of the material divided by the density (specific         gravity) of the material at the temperature of viscosity         measurement; kinematic viscosity is commonly measured in stokes         (St) or centistokes (cSt).     -   Pour point, measured for example by the ASTM D97 method, is the         temperature, to the next 5° F. increment, above which an oil or         distillate fuel becomes solid. The pour point is also the lowest         temperature, in 5° F. increments, at which the fluid will flow.         After preliminary heating, the sample is cooled at a specified         rate and examined at intervals of 3° C. for flow         characteristics. The lowest temperature at which movement of the         specimen is observed is recorded as the pour point.     -   Reid vapor pressure (RVP), measured for example by the ASTM D323         method, is the measure of the vapor pressure exerted by an oil,         mixed with a standard volume ratio of air, at 100° F. (38° C.).     -   Carbon residue, measured for example by the ASTM D189, D4536         methods, is the percentage of carbon by weight for coke,         asphalt, and heavy fuels found by evaporating oil to dryness         under standard laboratory conditions. Carbon residue is         generally termed Conradson Carbon Residue, or CCR.     -   Sulfur is the percentage by weight, or in parts per million by         weight, of total sulfur contained in a liquid hydrocarbon         sample. Sulfur must be removed from refined product to prevent         corrosion, protect catalysts, and prevent environmental         pollution. Sulfur is measured, for example, by ASTM D4294,         D2622, D5453 methods for gasoline and diesel range hydrocarbons.     -   Nitrogen, measured for example by the ASTM D4629, D5762 methods,         is the weight in parts per million, of total nitrogen contained         in a liquid hydrocarbon sample. Nitrogen compounds are also         catalyst poisons.     -   Various metals (arsenic, lead, nickel, vanadium, etc.) in a         liquid hydrocarbon are potential process catalyst poisons. They         are measured by Induced Coupled Plasma and/or Atomic Absorption         Spectroscopic methods, in ppm.     -   Salt is measured, for example, by the ASTM D3230 method and is         expressed as pounds of salt (NaCl) per 1000 barrels of crude.         Salts are removed prior to crude oil distillation to prevent         corrosion and catalyst poisoning.     -   Hydrogen sulfide (H₂S) is a toxic gas that can be evolved from         crude or products in storage or in the processing of crude.         Hydrogen sulfide dissolved in a crude stream or product stream         is measured in ppm.     -   Total acidity is measured, for example, by the ASTM methods,         D664, D974, and is a measure of the acidity or alkalinity of an         oil. The number is the mass in milligrams of the amount of acid         (HCl) or base (KOH) required to neutralize one gram of oil.

These properties affect the transportation and storage requirements for crudes, define the products that can be extracted under various processing schemes, and alert us to safety and environmental concerns. Each property can also affect the price that the refiner is willing to pay for the crude. In general, light, low sulfur crudes are worth more than heavy, high sulfur crudes because of the increased volume of premium products (gasoline, jet fuel, and diesel) that are available with minimum processing.

Crude Assays

A crude assay is a set of data that defines crude composition and properties, yields, and the composition and properties of fractions. Crude assays are the systematic compilation of data defining composition and properties of the whole crude along with yields and composition and properties of various boiling fractions. For example, a conventional assay method requires approximately 20 liters of crude oil be transported to a laboratory, which itself can be time-consuming and expensive, and then distilled to obtain the fractions and then have analysis performed on the fractions. This systematic compilation of data provides a common basis for the comparison of crudes. The consistent presentation of data allows us to make informed decisions as to storage and transportation needs, processing requirements, product expectations, crude relative values, and safety and environmental concerns. It also allows us to monitor crude quality from a single individual source over a period of time.

Crude oils or fractions are evaluated and compared using some of the key properties that are indicative of their performance in engines. These are the cetane number, the cloud point, the pour point (discussed above), the aniline point, and the flash point. In instances where the crude is suitable for production of gasoline, the octane number is another key property. These are described individually herein.

The cetane number of diesel fuel oil, determined by the ASTM D613 method, provides a measure of the ignition quality of diesel fuel; as determined in a standard single cylinder test engine; which measures ignition delay compared to primary reference fuels. The higher the cetane number; the easier the high-speed; direct-injection engine will start; and the less white smoking and diesel knock after start-up are. The cetane number of a diesel fuel oil is determined by comparing its combustion characteristics in a test engine with those for blends of reference fuels of known cetane number under standard operating conditions. This is accomplished using the bracketing hand wheel procedure which varies the compression ratio (hand wheel reading) for the sample and each of the two bracketing reference fuels to obtain a specific ignition delay, thus permitting interpolation of cetane number in terms of hand wheel reading.

The cloud point, determined by the ASTM D2500 method, is the temperature at which a cloud of wax crystals appears when a lubricant or distillate fuel is cooled under standard conditions. Cloud point indicates the tendency of the material to plug filters or small orifices under cold weather conditions. The specimen is cooled at a specified rate and examined periodically. The temperature at which cloud is first observed at the bottom of the test jar is recorded as the cloud point. This test method covers only petroleum products and biodiesel fuels that are transparent in 40 mm thick layers, and with a cloud point below 49° C.

The aniline point, determined by the ASTM D611 method, is the lowest temperature at which equal volumes of aniline and hydrocarbon fuel or lubricant base stock are completely miscible. A measure of the aromatic content of a hydrocarbon blend is used to predict the solvency of a base stock or the cetane number of a distillate fuel. Specified volumes of aniline and sample, or aniline and sample plus n-heptane, are placed in a tube and mixed mechanically. The mixture is heated at a controlled rate until the two phases become miscible. The mixture is then cooled at a controlled rate and the temperature at which two separate phases are again formed is recorded as the aniline point or mixed aniline point.

The flash point, determined by ASTM D56, D92, D93 methods, is the minimum temperature at which a fluid will support instantaneous combustion (a flash) but before it will burn continuously (fire point). Flash point is an important indicator of the fire and explosion hazards associated with a petroleum product.

The octane number, determined by the ASTM D2699 or D2700 methods, is a measure of a fuel's ability to prevent detonation in a spark ignition engine. Measured in a standard single-cylinder; variable-compression-ratio engine by comparison with primary reference fuels. Under mild conditions, the engine measures research octane number (RON), while under severe conditions, the engine measures motor octane number (MON). Where the law requires posting of octane numbers on dispensing pumps, the antiknock index (AKI) is used. This is the arithmetic average of RON and MON, (R+M)/2. It approximates the road octane number, which is a measure of how an average car responds to the fuel.

New rapid, and direct methods to help better understand crude oil compositions and properties from analysis of whole crude oil will save producers, marketers, refiners and/or other crude oil users substantial expense, effort and time. Therefore, a need exists for an improved system and method for determining indicative properties of crude oil fractions from different sources.

SUMMARY

Systems and methods are disclosed for providing virtual assays including assigned assay values pertaining to an oil sample subject to analysis, and its fractions, based on data obtained by analytic characterization of the oil sample without fractionation, and the density of the oil sample. The virtual assay of the oil sample provides a full range of information about fractions of the oil sample including naphtha, gas oil, vacuum gas oil, residue, and other information about the properties of the oil sample. This virtual assay is useful for producers, refiners, and marketers to benchmark the oil quality and, as a result, evaluate the oils without performing the customary extensive and time-consuming crude oil assays.

In an embodiment, the present disclosure is directed to a method for producing a virtual assay of an oil sample, wherein the oil sample is characterized by a density, selected from the group consisting of crude oil, bitumen and shale oil, and characterized by naphtha, gas oil, vacuum gas oil and vacuum residue fractions. Fourier transform ion cyclotron resonance (FT-ICR) mass spectroscopy data indicative of intensities at corresponding double bond equivalent (DBE) values over a range of DBE values for a solution of the oil sample without distillation in a FT-ICR mass spectroscopy solvent, is entered into a computer. An analytical value (AV) is calculated and assigned as a function of the FT-ICR mass spectroscopy data. Virtual assay data of the oil sample and the naphtha, gas oil, vacuum gas oil and vacuum residue fractions is calculated and assigned as a function of the AV and the density of the oil sample. The virtual assay data comprises a plurality of assigned data values.

In certain embodiments, the virtual assay data comprises: a plurality of assigned assay data values pertaining to the oil sample including one or more of the aromatic content, C5-asphaltenes content, elemental compositions of sulfur and nitrogen, micro-carbon residue content, total acid number and viscosity, a plurality of assigned assay values pertaining to the vacuum residue fraction of the oil sample including one or more of the elemental composition of sulfur and micro-carbon residue content; a plurality of assigned assay values pertaining to the vacuum gas oil fraction of the oil sample including one or both of the elemental compositions of sulfur and nitrogen; a plurality of assigned assay values pertaining to the gas oil fraction of the oil sample including one or more of the elemental compositions of sulfur and nitrogen, viscosity, and indicative properties including aniline point, cetane number, cloud point and/or pour point; and a plurality of assigned assay values pertaining to the naphtha fraction of the oil sample including one or more of the aromatic content, elemental composition of hydrogen and/or sulfur, paraffin content and octane number.

In certain embodiments, the virtual assay data also comprises: yields of fractions from the oil sample as mass fractions of boiling point ranges, including one or more of naphtha, gas oil, vacuum gas oil and vacuum residue; composition information of hydrogen sulfide and/or mercaptans in the oil sample and/or its fractions; elemental compositions of one or more of carbon, hydrogen, nickel, and vanadium; physical properties of the oil sample and/or its fractions including one or more of API gravity and refractive index; and/or indicative properties of the oil sample and/or its fractions including one or more of flash point, freezing point and smoke point.

In certain embodiments, the method further comprises operating a FT-ICR mass spectrometer to obtain the FT-ICR mass spectroscopy data indicative of intensities at corresponding double bond equivalent (DBE) values over a range of DBE values, by carrying out spectroscopy on the solution of the oil sample without distillation in the FT-ICR mass spectroscopy solvent.

In certain embodiments, each assay value is determined by a multi-variable polynomial equation with predetermined constant coefficients developed using linear regression techniques, wherein corresponding variables are the AV and the density of the oil sample.

In certain embodiments, the analytical value is an index derived from a summation of peak intensities over a range of DBE values.

In an embodiment, the present disclosure is directed to a system for producing a virtual assay of an oil sample, wherein the oil sample is characterized by a density, is selected from the group consisting of crude oil, bitumen and shale oil, and is characterized by naphtha, gas oil, vacuum gas oil and vacuum residue fractions. The system comprises a FT-ICR mass spectrometer that outputs FT-ICR mass spectroscopy data, a non-volatile memory device, a processor coupled to the non-volatile memory device, and first and second calculation modules that are stored in the non-volatile memory device and that are executed by the processor. The non-volatile memory device stores the calculation module and data, the data including the FT-ICR mass spectrometer data that is indicative of intensities at corresponding double bond equivalent (DBE) values over a range of DBE values for a solution of the oil sample without distillation in a FT-ICR mass spectrometer solvent. The first calculation module contains suitable instructions to calculate, as a function of the FT-ICR mass spectrometer data, one or more analytical values (AV). The second calculation module contains suitable instructions to calculate, as a function of the one or more AVs and the density of the oil sample, a plurality of assigned data values as the virtual assay pertaining to the overall oil sample, and the naphtha, gas oil, vacuum gas oil and vacuum residue fractions of the oil sample.

Still other aspects, embodiments, and advantages of these exemplary aspects and embodiments, are discussed in detail below. Moreover, it is to be understood that both the foregoing information and the following detailed description are merely illustrative examples of various aspects and embodiments, and are intended to provide an overview or framework for understanding the nature and character of the claimed aspects and embodiments. The accompanying drawings are included to provide illustration and a further understanding of the various aspects and embodiments, and are incorporated in and constitute a part of this specification. The drawings, together with the remainder of the specification, serve to explain principles and operations of the described and claimed aspects and embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is process flow diagram of steps used to implement the method described herein for providing virtual assays of an oil sample such as crude oil based on Fourier transform ion cyclotron resonance mass spectroscopy.

FIG. 2 is a graphic plot of typical Fourier transform ion cyclotron resonance mass spectroscopy data for two types of crude oil including an oil sample under investigation in the example herein.

FIG. 3 is a process flow diagram of steps used in an example herein to provide a virtual assay of a crude oil sample based on Fourier transform ion cyclotron resonance mass spectroscopy.

FIG. 4 is a block diagram of a component of a system for implementing the invention, according to one embodiment.

DETAILED DESCRIPTION

Systems and methods are disclosed for providing virtual assays of an oil sample such as crude oil based on Fourier transform ion cyclotron resonance (FT-ICR) mass spectroscopy carried out on the oil sample, and the density of the oil sample. The virtual assay provides a full range of information about fractions of the oil sample including naphtha, gas oil, vacuum gas oil, vacuum residue, and other information about the properties of the oil sample. Using the system and method herein, the virtual assay data pertaining to these several fractions of the oil sample and the oil sample itself are obtained without fractionation of the oil sample into the several components.

Concerning the naphtha fraction, assigned assay values for the virtual assay include: elemental composition values included in the virtual assay comprise one or more of hydrogen content, aromatic content, paraffin content and sulfur content; and an indicative property included in the virtual assay comprises an octane number. Concerning the gas oil fraction, assigned assay values for the virtual assay include: elemental composition values included in the virtual assay comprise one or more of sulfur content and nitrogen content; physical properties included in the virtual assay comprises viscosity and pour point; and indicative properties included in the virtual assay comprise one or more of aniline point, cetane number and cloud point. Concerning the vacuum gas oil fraction, assigned assay values for the virtual assay include: elemental composition values included in the virtual assay comprise one or more of sulfur content, nitrogen content and micro carbon residue content. Concerning the vacuum residue, assigned assay values for the virtual assay include: elemental composition values included in the virtual assay comprise one or more of sulfur content and micro carbon residue content. Concerning the full range of the oil sample, assigned assay values for the virtual assay include: elemental composition values included in the virtual assay comprise one or more of asphaltene content, sulfur content, nitrogen content and total acids content (total acid number, mg KOH/100 g); and physical properties included in the virtual assay comprises viscosity and pour point.

In certain embodiments of the virtual assay provided herein, the “naphtha fraction” refers to a straight run fractions from atmospheric distillation containing hydrocarbons having a nominal boiling range of about 20-205, 20-193, 20-190, 20-180, 20-170, 32-205, 32-193, 32-190, 32-180, 32-170, 36-205, 36-193, 36-190, 36-180 or 36-170° C.; the “gas oil fraction” refers to a straight run fractions from atmospheric distillation containing hydrocarbons having a nominal boiling range of about 170-400, 170-380, 170-370, 170-360, 180-400, 180-380, 180-370, 180-360, 190-400, 190-380, 190-370, 190-360, 193-400, 193-380, 193-370 or 193-360° C.; the “vacuum gas oil fraction” refers to a straight run fractions from vacuum distillation containing hydrocarbons having a nominal boiling range of about 360-565, 360-550, 360-540, 360-530, 360-520, 360-510, 370-565, 370-550, 370-540, 370-530, 370-520, 370-510, 380-565, 380-550, 380-540, 380-530, 380-520, 380-510, 400-565, 400-550, 400-540, 400-530, 400-520 or 400-510° C.; and “vacuum residue” refers to the bottom hydrocarbons from vacuum distillation having an initial boiling point corresponding to the end point of the VGO range hydrocarbons, for example about 510, 520, 530, 540, 550 or 565° C., and having an end point based on the characteristics of the crude oil feed.

The system and method is applicable for naturally occurring hydrocarbons derived from crude oils, bitumens or shale oils, and heavy oils from refinery process units including hydrotreating, hydroprocessing, fluid catalytic cracking, coking, and visbreaking or coal liquefaction. Samples can be obtained from various sources, including an oil well, core cuttings, oil well drilling cuttings, stabilizer, extractor, or distillation tower. In certain embodiments system and method is applicable for crude oil, whereby a virtual assay is obtained using the systems and methods herein without the extensive laboratory work required for distillation and analysis of each of the individual fractions.

Referring to FIG. 1 , a process flow diagram of steps carried out to obtain a virtual assay 195 is provided. Prior to carrying out the steps outlined in FIG. 1 , a set of constants is obtained for each of the elemental composition values/physical properties/indicative properties to be calculated using the process and system disclosed herein to obtain a virtual assay, represented as dataset 105. The set of constants can be developed, for instance by linear regression techniques, based on empirical data of a plurality of crude oil assays and analyses using conventional techniques including distillation and industry-established testing methods to obtain the crude oil assay data. An exemplary set of constants used for calculating assigned assay values to produce the virtual assay 195 is provided herein.

At step 110, the density if the oil sample is provided (steps for obtaining this density are not shown and can be carried out as is known, in certain embodiments a 15° C./4° C. density in units of kilograms per liter using the method described in ASTM D4052); this density value can be stored in memory with other data pertaining to the oil sample, or conveyed directly to the one or more steps as part of the functions thereof. In step 115, if necessary, the oil sample is prepared for a particular analytic characterization technique (shown in dashed lines as optional). In step 120, analytic characterization of the oil sample, or the oil sample prepared as in step 115, without fractionation, is carried out. As a result, analytic characterization data 125 is obtained.

In step 130, the analytic characterization data 125 is used to calculate one or more analytical values 135, which are one common analytical value or a common set of analytical values used in subsequent steps to calculate a plurality of different elemental composition values/physical properties/indicative properties that make up the virtual assay. In the embodiments herein the one common analytical value or common set of analytical values is an index or plural index values, also referred to as a FTMS index (or FTMSI), derived from a summation of peak intensities over a range of DBE values, as determined by FT-ICR mass spectroscopy carried out on the oil sample.

Steps 140, 150, 160, 170 and 180 are used to calculate and assign a plurality of different elemental composition values/physical properties/indicative properties that make up the virtual assay 195, for each of a total oil sample, a naphtha fraction, a gas oil fraction, a vacuum gas oil fraction and a vacuum residue fraction, respectively. Each of the steps produces corresponding assigned assay values for the virtual assay 195, include including assigned assay values 145 pertaining to the total oil sample, assigned assay values 155 pertaining to a naphtha fraction, assigned assay values 165 pertaining to a gas oil fraction, assigned assay values 175 pertaining to a vacuum gas oil fraction and assigned assay values 185 pertaining to a vacuum residue fraction.

In certain embodiments, the steps are carried out in any predetermined sequence, or in no particular sequence, depending on the procedures in the calculation modules. In certain embodiments, the steps are carried out in parallel. The process herein uses a common analytical value, in conjunction with the set of constants and the density of the oil sample, for each of the assigned assay values (elemental composition values/physical properties/indicative properties) in the given virtual oil sample assay 195 produced at step 190. For instance, each of the steps 140, 150, 160, 170 and 180 are carried in any sequence and/or in parallel out as show using the equations herein for various analytical values or sets of analytical values.

The assigned assay values from each of the fractions and the total oil sample are compiled and presented as a virtual assay 195, which can be, for instance, printed or rendered on a display visible to, or otherwise communicated to, a user to understand the composition and properties of the crude. With the virtual assay 195, users such as customers, producers, refiners, and marketers can benchmark the oil quality. The virtual assay 195 can be used to guide decisions related to an appropriate refinery or refining unit, for processing the oil from which the oil sample is obtained, and/or for processing one or more of the fractions thereof. In addition the assigned assay values including the indicative properties are used to determine the engine/fuel performance or usability or flow characteristic or composition. This can be accomplished using the method and system herein without performing the customary extensive and time-consuming crude oil assays.

The assigned assay values for the virtual assay herein are calculated as a function of one or more analytical values, and the density of the oil sample, as denoted at (1).

AD=f(ρ,AV)  (1)

-   -   where:         -   AD is the assigned assay value (for example a value and/or             property representative of an elemental composition value, a             physical property or an indicative property); and         -   AV is an analytical value of the oil sample, wherein AV can             be a single analytical value, or wherein AV can be AV(1) . .             . AV(n) as plural analytical values of the oil sample,             wherein n is an integer of 2 or more, in certain embodiments             2, 3 or 4 analytical value; and         -   ρ is the density of the oil sample, in certain embodiments a             15° C./4° C. density in units of kilograms per liter using             the method described in ASTM D4052.

According to an embodiment of the system and method described herein, an analytical value AV is a singe value, an index value derived from a summation of peak intensities over a range of DBE values, as determined by FT-ICR mass spectroscopy carried out on the oil sample, represented herein as a FTMS index. Advantageously, the method and system herein deploy analytical characterization by FT-ICR mass spectroscopy to carry out analysis of the oil sample without fractionating, obtain an analytical value or set of analytical values based on the FT-ICR mass spectroscopy analysis of the oil sample, and use the analytical value or set of analytical values, and the density of the oil sample, to obtain a plurality of assigned assay values (for example a value and/or property representative of an elemental composition value, a physical property or an indicative property) to produce a virtual assay of the oil sample.

In one embodiment, an assigned assay value is calculated used a third degree multi variable polynomial equation including the analytical value, the density of the oil sample, and a plurality of constants, for example predetermined by linear regression, as denoted in equation (2a).

AD=K _(AD) +X1_(AD) *AV+X2_(AD) *AV ² +X3_(AD) *AV ³ +X4_(AD) *ρ*AV  (2a)

-   -   where:         -   AD is the assigned assay value (for example a value and/or             property representative of an elemental composition value, a             physical property or an indicative property);         -   AV is an analytical value of the oil sample;         -   ρ is the density of the oil sample, in certain embodiments a             15° C./4° C. density in units of kilograms per liter using             the method described in ASTM D4052; and         -   K_(AD), X1_(AD), X2_(AD), X3_(AD), and X4_(AD) are             constants, for instance, developed using linear regression             techniques (note that in certain embodiments and for certain             assigned assay values, one or more of K_(AD), X1_(AD),             X2_(AD), X3_(AD) and X4_(AD) is/are not used, or is/are             zero).

In another embodiment, an assigned assay value is calculated used a third degree multi variable polynomial equation including the analytical value, the density of the oil sample, and a plurality of constants, for example predetermined by linear regression, as denoted in equation (2b).

AD=K _(AD) +X1_(AD) *ρ+X2_(AD)*ρ₂ +X3_(AD)*ρ³ +X4_(AD) *AV+X5_(AD) *AV ² +X6_(AD) *AV ³ +X7_(AD) *ρ*AV  (2b)

-   -   where:         -   AD is the assigned assay value (for example a value and/or             property representative of an elemental composition value, a             physical property or an indicative property);         -   AV is an analytical value of the oil sample;         -   ρ is the density of the oil sample, in certain embodiments a             15° C./4° C. density in units of kilograms per liter using             the method described in ASTM D4052; and         -   K_(AD), X1_(AD), X2_(AD), X3_(AD), X4_(AD), X5_(AD), X6_(AD)             and X7_(AD) are constants, for instance, developed using             linear regression techniques (note that in certain             embodiments and for certain assigned assay values, one or             more of K_(AD), X1_(AD), X2_(AD), X3_(AD), X4_(AD), X5_(AD),             X6_(AD) and X7_(AD) is/are not used, or is/are zero).

Assigned assay values that can be determined and included for display or presentation to the user in the virtual assay produced using the systems and methods herein include one or more of:

-   -   elemental composition of the oil sample and its fractions         including the sulfur and nitrogen compositions;     -   TAN (total acid number) of the oil sample;     -   composition of certain desirable and undesirable compounds or         types of compounds present in the oil sample and/or its         fractions, including one or more of, micro carbon residue,         C5-asphaltenes (the yield of asphaltenes using separation based         on C5 paraffins as deasphalting solvent), paraffins, aromatics,         and naphthenes;     -   physical properties of the oil sample and/or its fractions         including viscosity such as kinematic viscosity;     -   indicative properties of the oil sample and/or its fractions,         including one or more of cloud point, pour point, research         octane number, cetane number and aniline point.

In certain embodiments, the assigned assay values can include yields of fractions from the oil sample, for example as mass fractions of boiling point ranges, including one or more of naphtha, gas oil, vacuum gas oil and vacuum residue. In certain embodiments, the assigned assay values can include composition information of hydrogen sulfide and/or mercaptans in the oil sample and/or its fractions. In certain embodiments, the assigned assay values can include elemental compositions of one or more of carbon, hydrogen, nickel, and vanadium. In certain embodiments, the assigned assay values can include physical properties of the oil sample and/or its fractions including one or more of API gravity and refractive index. In certain embodiments, the assigned assay values can include indicative properties of the oil sample and/or its fractions including one or more of flash point, freezing point and smoke point.

In certain embodiments, a method for producing a virtual assay of an uncharacterized oil sample is provided. The uncharacterized oil sample is characterized by a density, selected from the group consisting of crude oil, bitumen and shale oil, and characterized by naphtha, gas oil, vacuum gas oil and vacuum residue fractions. The virtual assay comprises a plurality of assigned data values. The uncharacterized oil sample is obtained, for instance the sample being between one to two milliliters in volume and not subject to any fractionation. A plurality of known data values (corresponding to the assigned data values used in the virtual assay) for known oil samples with known densities (which known oil samples exclude the uncharacterized oil sample) are obtained. This data is obtained from empirical data of a plurality of existing crude oil assays and/or analyses using conventional techniques including distillation and industry-established testing methods. One or more selected analytical techniques are carried out on the each of the known oil samples, and one or more analytical values are calculated for each of the known oil samples. The one or more selected analytical techniques are carried out on the uncharacterized oil sample, and one or more analytical values are calculated for the uncharacterized oil sample. Constants of a polynomial equation are obtained, and the polynomial equation is used to determine a plurality of assigned data values that make up the virtual assay of the uncharacterized oil sample. The polynomial equation is a function of density and the one or more analytical values of the uncharacterized oil sample. The constants of the polynomial equation are determined using a fitting method to fit the plurality of known data values of the plurality of known oil samples to the plurality of values of the density of the plurality of known oil samples and the plurality of the one or more analytical values for the plurality of known oil samples.

Rather than relying on conventional techniques including distillation and laborious, costly and time-consuming analytical methods to measure/identify data regarding the crude oil and/or its fractions including elemental composition, physical properties and indicative properties, as little as 1 gram of oil can be analyzed. From the analysis of a relatively small quantity of the oil sample, the assigned assay values are determined by direct calculation, without requiring distillation/fractionization.

FT-ICR mass spectroscopy is the analytic characterization technique that is employed on a relatively small quality of an oil sample, such as crude oil. An analytical value, comprising or consisting of the FTMS index from said analytic characterization technique, is used to calculate and assign physical and indicative properties that are the requisite data for the virtual oil sample assay. The method and system provides insight into the properties of oil sample, the naphtha fraction, the gas oil fraction, the vacuum gas oil fraction, and the vacuum residue fraction, without fractionation/distillation (conventional crude oil assays). The virtual oil sample assay will help producers, refiners, and marketers benchmark the oil quality and, as a result, evaluate (qualitatively and economically) the oils without going through costly and time consuming crude oil assays. Whereas a conventional crude oil assay method could take up to two months, the method and system herein can provide a virtual assay in less than one day and in certain embodiments less than 1-2 hours. In addition, the method and system herein carried out at 1% or less of the cost of a traditional assay requiring distillation/fractionization follows by individual testing for each type of property and for each fraction.

The systems and methods herein are implemented using an index derived from Fourier transform ion cyclotron resonance (FT-ICR) mass spectroscopy data as an analytical value in equations (1), and (2a) or (2b), above. Embodiments of such methods are described in the context of assigning an indicative property of a fraction of an oil sample in commonly owned U.S. Ser. No. 10/725,013B2, which is incorporated by reference herein in its entirety. In the systems and methods herein, and with reference to FIG. 1 , a virtual assay 195 of an oil sample is obtained at step 190, wherein each assigned data value of the virtual assay is a function of an index derived from FT-ICR mass spectroscopy data based on analysis of the oil sample (or in another embodiment, as a function of the density of the oil sample and an index derived from FT-ICR mass spectroscopy data based on analysis of the oil sample). The virtual assay provides information about the oil sample and fractions thereof to help producers, refiners, and marketers benchmark the oil quality and, as a result, evaluate the oils without performing the customary extensive and time-consuming crude oil assays involving fractionation/distillation and several individual and discrete tests.

The oil sample is prepared, step 115, by dissolving the oil sample in a suitable FT-ICR mass spectroscopy solvent. In certain embodiments, as is known in the field of FT-ICR mass spectroscopy, plural oil solutions are prepared and analyzed in sequence for calibration and optimization of the results. For example, a small quantity (for instance, 50-500, 50-200 or 50-150 μL) of an oil sample is dissolved in an effective quantity of a suitable solvent, such as 5-50, 5-20 or 5-50 mL to form a first solution. The solvent for the first solution can be toluene, or a suitable mixture of 70-30, 60-40 or about 50 V % of toluene, and about 30-70, 40-60 or about 50 V % of methanol, methylene chloride, dichloromethane and/or tetrahydrofuran. If complete solubility is not attained, based upon visual observation against a light source, methylene chloride is added to achieve a clear solution. The solution is shaken for a period of at least 10-30 seconds. A second solution is prepared with a suitable dilution of solution 1 in methylene chloride, for instance in the range (V/V) of about 1:50-1:200, 1:50-1:150, or 1:80-1:120, wherein miscibility of the solvent mix is ensured. A third solution is prepared with a suitable dilution of the second solution in methylene chloride, for example 1:5-1:20, 1:5-1:15 or 1:8-1:12. The dilution ratio depends on the sample and is typically determined empirically on a case-by-case basis, starting from the third solution, advancing to the second solution and then to the first solution.

In a typical operation, for each analysis of an oil sample, the operator tunes the spectrometer settings to optimize performance. The performance of the FT-ICR MS instrument is checked by obtaining a mass calibration in ESI positive mode. This ESI calibration can be used in the APPI mode by exchanging the ESI ion source with the APPI source. The analysis begins with the third solution, which is directly infused into the mass calibrated FT-ICR MS APPI source by a syringe pump. The operator records and averages 100 accumulated scans, which serve as a general basis for fine-tuning the instrument parameters. If sufficient signal intensity (108 to 109 units) is not obtained with the third solution, the analysis is repeated with the second solution. If the analysis with the second solution still does not yield sufficient signal intensity, the analysis is repeated with the first solution. The operator checks the signal shape at the beginning, middle and end of the mass range, and calibrates accordingly until a suitable independent signal shape is obtained. Using software packages known in FT-ICR MS technology, FT-ICR MS data is provided including the intensities of detected peaks for a series of double bond equivalent (DBE) values.

The prepared solution of the oil sample is analyzed, step 120, and FT-ICR MS data is obtained. Step 120 is carried out and the analytic characterization data, the FT-ICR mass spectroscopy data, is entered into the computer system 400 described herein with respect to FIG. 4 , for example stored into non-volatile memory of the via data storage memory 480, represented as the analytic characterization data 125. This can be carried out by a raw data receiving module stored in the program storage memory 470.

The peak intensities over the range of DBE value compounds in the oil sample are included in the analytic characterization data, to calculate an analytical value, step 130, as an index. Step 130 is carried out, for example, by execution by the processor 420 of one or more modules stored in the program storage memory 470, and the analytical values 135, the index, is stored in the program storage memory 470 or the data storage memory 480, for use in the modules determining the assigned data values. In certain embodiments, the density of the oil sample, provided at step 110, is stored in the program storage memory 470 or the data storage memory 480, for use in the modules determining the assigned data values; this can be carried out by a raw data receiving module stored in the program storage memory 470.

The assigned data values including virtual assay data 145 pertaining to the total oil sample, virtual assay data 155 pertaining to a naphtha fraction, virtual assay data 165 pertaining to a gas oil fraction, virtual assay data 175 pertaining to a vacuum gas oil fraction and virtual assay data 185 pertaining to vacuum residue fraction, are obtained according to the functions described herein, for example, in the corresponding steps 140, 150, 160, 170 and 180. The constants used for determining the assigned data values, are provided at step 105 and are stored in the program storage memory 470 or the data storage memory 480, for use in the modules determining the assigned data values. The steps for obtaining the assigned data values are carried out, for example, by execution by the processor 420 of one or more modules stored in the program storage memory 470, and the several assigned data values are calculated and stored in the data storage memory 480, presented on the display 410 and/or presented to the user by some other output device such as a printer.

A number of ionization sources have been used in FT-ICR-MS, with some being preferable for gases, others for liquids, and others for solids. Ionization sources for FT-ICR-MS include electron ionization (EI), which uses a glowing filament, which may break down the molecules under study. Inductively coupled plasma ionization (ICP) is a destructive technique which applies heat to reduce a sample to its atomic components. Chemical ionization (CI), a subset of EI, adds gases such as methane, isobutane, or ammonia, producing results that are less damaging to the molecules under study. Direct analysis in real time (DART) ionizes samples at atmospheric pressure using an electron beam. Matrix-assisted, laser desorption ionization (MALDI) is a solid phase process that uses laser energy to ionize molecules off a metal target plate. Electrospray ionization (ESI), is a liquid phase process that produces a fine mist of droplets, as from an atomizer.

FT-ICR MS frequently relies on ESI or on a related variant, such as atmospheric pressure chemical ionization (APCI) or atmospheric pressure photoionization (APPI). APCI uses a corona discharge from an electrified needle to induce ionization of a solvent, which in turn reacts with the sample molecules to induce a chemical reaction resulting in an ionized sample molecule. APPI uses a photon discharge from high-intensity ultraviolet light to ionize the solvent gas, which in turn ionizes the sample molecules. APCI works well with relatively small, neutral, or hydrophobic compounds, such as steroids, lipids, and non-polar drugs. APPI works well with highly non-polar molecules like napthols and anthracenes.

In the petroleum industry, FT-ICR is conducted using ESI, and in certain operations the APPI variant of ESI. A petroleum sample is diluted in an appropriate solvent and infused into the spectrometer. The liquid sample is evaporated and the components are ionized by ESI or APPI, yielding unfragmented gas phase ions of the sample components. These ions are trapped in the strong magnetic field of the mass analyzer, where their mass-to-charge ratios (m/z) are determined with high resolution and accuracy. The spectrometer provides a resolution of R>300,000 at m/z 400, which is high enough for routinely separating signals spaced as closely as 3.4 mDa (SH₄ vs. ¹²C₃), which is essential for the correct assignment of the elemental composition (C_(c)H_(h)N_(n)O_(o)S_(s)Ni_(i)V_(v)) corresponding to each mass signal in petroleum samples. The identified elemental compositions are then classified according to the heteroatoms in their elemental composition, e.g., pure hydrocarbons, mono-sulfur (or mono-nitrogen) species for molecules with one sulfur (or nitrogen) atom, or molecules with any combination of heteroatoms. The corresponding double bond equivalent (DBE) values and carbon numbers are calculated for each identified elemental composition, where the DBE is defined as half the number of hydrogen atoms lacking from a completely saturated molecule with an otherwise identical number of carbon and heteroatoms.

In the process herein, peak intensities over a range of DBE values are obtained from the FT-ICR MS data from analysis of oil samples such as crude oil and can be used to formulate one or more indices to calculate the various assigned data values for the virtual assay herein. This information can be obtained relatively rapidly and inexpensively from an FT-ICR MS operation as compared to the prior art assay methods described above.

The determination of the assigned data is carried out using variables comprising or consisting of the FTMSI of the oil sample and the density of the oil sample.

AD=f(ρ,FTMSI))  (3)

-   -   where:         -   AD is the assigned data value (for example a value and/or             property representative of an elemental composition value, a             physical property or an indicative property);         -   FTMSI=index derived from a summation of peak intensities             over a range of DBE values, as determined by FT-ICR MS             analysis of the oil sample; and         -   ρ is the density of the oil sample, in certain embodiments a             15° C./4° C. density in units of kilograms per liter using             the method described in ASTM D4052.

For example, this relationship can be expressed as follows:

AD=K _(AD) +X1_(AD)*FTMSI+X2_(AD)*FTMSI² +X3_(AD)*FTMSI³ +X4_(AD)*ρ*FTMSI  (4)

-   -   where AD, FTMSI and p are as in equation (3), and where:         -   K_(AD), X1_(AD), X2_(AD), X3_(AD) and X4_(AD) are constants,             for instance, developed using linear regression techniques,             for each AD to be determined (note that in certain             embodiments and for certain assigned assay values, one or             more of K_(AD), X1_(AD), X2_(AD), X3_(AD), X4_(AD) and             X4_(AD) is/are not used, or is/are zero).

Using the equation (4), one or more assigned data values AD are determined using the density of the oil sample and the FTMSI of the oil sample, as determined by FT-ICR mass spectroscopy of the oil sample.

Table 3 lists assigned data for a virtual assay of an oil sample under investigation, with descriptions, abbreviations and units, for each assigned data property for the naphtha fraction, the gas oil fraction, the vacuum gas oil fraction, the vacuum residue fraction and the overall oil sample. Table 3 further provides exemplary constants, for instance, developed using linear regression techniques, for plural assigned data values to be determined based on the density of the oil sample and the FTMSI of the oil sample. These constants are used in the example below with the calculated values provided in Table 5 compared to the actual values as determined by a conventional crude oil assay.

The constants, for example as in Table 3, are stored as in step 105 in the process flow diagram of FIG. 1 . These are used in one or more calculation modules to obtain the virtual assay 195 of an oil sample as in step 190, in conjunction with the analytical values obtained step 130 based upon FT-ICR mass spectroscopy data, the FTMS index of the oil sample. In certain embodiments the constants are stored as in step 105, and the density is stored as in step 110; the constants are used in one or more calculation modules to obtain the virtual assay 195 of an oil sample as in step 190, in conjunction with density of the oil sample stored in step 110 and the analytical values obtained in step 130 from the FT-ICR MS data obtained in step 120, the FTMS index of the oil sample. As shown, modules are separated based on the fraction for which assigned data values are obtained, but is it understood that they can be arranged in any manner so as to provide all of the assigned data values required for the virtual assay of the oil sample.

In certain embodiments, the assigned data values including virtual assay data 145 pertaining to the total oil sample, virtual assay data 155 pertaining to a naphtha fraction, virtual assay data 165 pertaining to a gas oil fraction, virtual assay data 175 pertaining to a vacuum gas oil fraction and virtual assay data 185 pertaining to a vacuum residue fraction. This data is obtained according to the function (3) described above (for example expressed as in equation (4) described above, for example, with the corresponding modules/steps 140, 150, 160, 170 and 180.

In certain embodiments, the analytical value obtained as in step 130 is a FTMSI of the oil sample determined as follows:

$\begin{matrix} {{FTMSI} = {\sum\limits_{{DBE} = \min}^{\max}{({Intensity})/\left( {{1E} + 9} \right)}}} & (5) \end{matrix}$

-   -   where:         -   intensity=the intensity for each double bond equivalent             (DBE) over a range of values min to max.

Example

Crude oil samples, including a crude oil sample as the oil sample under investigation, were prepared and analyzed by atmospheric pressure photo ionization (APPI) Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) according to the methods described herein. FIG. 2 is a graphic plot of typical FT-ICR MS data for two solutions of crude oil samples identified by API gravity, where the intensity is plotted against the double bond equivalence (DBE). FIG. 3 shows a process flow chart of steps for a method of obtaining assigned data based on FT-ICR MS data. In step 305, constants are obtained, for example corresponding to the data in Table 3. In step 310, the density of the oil sample is obtained.

In step 315, the oil sample is prepared. A stock solution 1 of the oil sample is prepared by dissolving a 100 μL sample of the crude oil in 10 mL of toluene (or alternatively, in a 50/50% volume mixture of toluene with methanol, methylene chloride, dichloromethane or tetrahydrofuran). If complete solubility is not attained, based upon visual observation against a light source, methylene chloride is added to achieve a clear solution. The solution is shaken for a minimum of 20 seconds. A solution 2 is prepared with a 1:100 dilution of solution 1 in methylene chloride. The miscibility of the solvent mix is ensured. A solution 3 is prepared with a 1:10 dilution of solution 2 in methylene chloride (for example, 100 μL of solution 2 in 900 μL solvent). The dilution ratio depends on the sample and has to be determined empirically on a case-by-case basis, starting from solution 3, then advancing to solution 2 and then to solution 1.

In step 320, analytic characterization of the oil sample prepared as in step 315, without fractionation, is carried out. As a result, analytic characterization data is obtained and stored at step 325. In the example, for each analysis of a sample, the operator tunes the spectrometer settings to optimize performance. Key parameters and default settings follow:

-   -   TD (Fid Size): 4M     -   Average Spectra: 100     -   Source Accumulation: 0.001 s     -   Ion Accumulation Time: 0.001 s     -   TOF (AQS): variable, depending on sample     -   APPI Temperature 250-400° C., depending on sample     -   Detection Mode: Broadband     -   Low Mass: 150 to 350 m/z     -   High Mass: 3000 m/z

The performance of the FT-ICR MS instrument is checked by obtaining a mass calibration in ESI positive mode. This ESI calibration can be used in the APPI mode by exchanging the ESI ion source with the APPI source. The mass calibration remains valid for one day of normal operation as long as the key instrument parameters described above have not been changed. A change of any of the key instrument parameters requires a complete recalibration by switching to the ESI source, calibration, followed by switching back to the APPI source.

At step 321, the analysis begins by infusing the solution into the FT-ICR MS APPI source, and verification of sufficient signal intensity. In the example, Solution 3 is directly infused into the mass calibrated FT-ICR MS APPI source by a syringe pump. The operator records and averages 100 accumulated scans, which serve as a general basis for fine-tuning the instrument parameters. If sufficient signal intensity (108 to 109 units) is not obtained with Solution 3, the analysis is repeated with Solution 2. If the analysis with Solution 2 still does not yield sufficient signal intensity, the analysis is repeated with Solution 1. The operator checks the signal shape at the beginning, middle and end of the mass range. An excessive sample load can be diagnosed by a signal splitting. In case of signal splitting, all signals will appear as two closely aligned signals or, in severe cases, even as a group of signals. When the operator observes such signal splitting, the sample should be diluted until a good independent signal shape is obtained. The following pass/fail criteria are applied to the tests: A mass calibration is acceptable when every mass calibrant in the mass range of the sample does not deviate more than ±0.2 ppm from the expected value, except calibrants that are discarded from the list due to either low intensity (below 3 times the baseline noise) or a calibrant signal that is overlapping a contamination signal.

At step 322, raw data is analyzed a peak list is sorted by increasing m/z values. Data processing is an extensive exercise involving four different software packages as described below. Data processing can significantly impact the quality of the produced data and therefore must be performed by, or under the direction of an experienced scientist. In Example F1, the trade names of the respective programs are followed by their sources. DataAcquisition from Bruker Daltonics of Bremen, Germany is one example. The raw data is checked for sufficient signal shape and intensity as described above and, if necessary, re-measured until sufficient signal shape and intensity are obtained. DataAnalysis from Bruker Daltonics of Bremen, Germany is also used. The recorded raw data file is loaded into the DataAnalysis software. The peak list is sorted according to increasing m/z values. The m/z values and intensities are then saved as a peak list “text file.” Composer from SierraAnalytics of Modesto, Calif. is used, wherein the peak lists are loaded into the Composer software. The Composer software is started and a suitable parameter file is loaded.

At step 323, a good fit of data is confirmed. Recalibration is checked by looking at the identified species. The individual series are inspected for consistency, for instance, for missing series and/or interrupted series, which may indicate non-ideal re-calibration. In exceptional cases, recalibration parameters are fine tuned until a good fit of the data is obtained. The main heteroatom classes, which are those constituting more than 1 percent of the assigned heteroatom classes, are exported into the Microsoft Excel spreadsheet “Automatic Processing Composer Data.xls.” Excel Spreadsheet Automatic Processing Composer Data is an in-house developed spreadsheet that processes the elemental compositions calculated by the Composer software and produces all graphs in a final reporting form. An Excel workbook with one summary tab and detail tabs for each identified heteroatom class is created. Peak intensity data is presented in Table 4, and is shown in FIG. 2 for different oil samples. One of the oil samples in FIG. 2 and the data in Table 4 is representative of the oil sample in this example, which is an Arabian medium crude with a 15° C./4° C. density of 0.8828 Kg/L, and an API gravity of 28.8°; this oil sample was analyzed by APPI FT-ICR MS, using the described method. FIG. 2 is a graphic plot of peak intensities for two types of crude oil as oil samples identified by API gravity, where the peak intensity is plotted against DBE value. All or a portion of the data in Table 4 and FIG. 2 is obtained and stored as the analytic characterization data in step 325.

At step 330, an analytical value, the FT-ICR MS index, FTMSI, is calculated by summing the intensities of the detected peaks stored at step 325, and then dividing by 1E+9, as in equation (5) above, with the FTMSI in the values reported in Table 4 calculated as 40.70753. Note that the denominator in this equation can be varied, for instance, by adjusting the constants.

The FTMSI, stored at step 335, is applied to step 390. At step 390, Equation (4) and the constants from Table 3 are applied for each of the listed ADs, using the FTMSI stored at step 335, the constants stored at step 305, and the density of the oil sample stored at step 310, as shown below. Each of the determined ADs can be added to a virtual assay 395 of the oil sample. For example, this can be carried out as one step, or as plural steps, for instance, similar to steps 140, 150, 160, 170 and 180 described herein in conjunction with FIG. 1 to calculate a plurality of different elemental composition values/physical properties/indicative properties that make up the virtual assay, for each of a total oil sample, a naphtha fraction, a gas oil fraction, a vacuum gas oil fraction and a vacuum residue fraction, respectively, and to produce the virtual oil sample assay 195 at step 190.

Equation (4) is applied to each of the ADs that make up the virtual assay including those identified in Table 3, using the corresponding units. In addition, the constants denoted in Table 3 are used as the constants K_(AD), X1_(AD), X2_(AD), X3_(AD) and X4_(AD)) in equation (4); the FTMSI index based on the data in Table 4, calculated as 40.70753 using equation (5) above, is used in equation (4); and the density p used in equation (4) for the of the oil sample under investigation is the 15° C./4° C. density in units of kilograms per liter using the method described in ASTM D4052, which is 0.8828 Kg/L. The calculated AD values are provided for the oil sample under investigation in Table 5, compared to the actual values obtained using a conventional crude oil assay.

FIG. 4 shows an exemplary block diagram of a computer system 400 in which one embodiment of the present invention can be implemented. Computer system 400 includes a processor 420, such as a central processing unit, an input/output interface 430 and support circuitry 440. In certain embodiments, where the computer system 400 requires a direct human interface, a display 410 and an input device 450 such as a keyboard, mouse, pointer, motion sensor, microphone and/or camera are also provided. The display 410, input device 450, processor 420, and support circuitry 440 are shown connected to a bus 490 which also connects to a memory 460. Memory 460 includes program storage memory 470 and data storage memory 480. Note that while computer system 400 is depicted with direct human interface components display 410 and input device 450, programming of modules and exportation of data can alternatively be accomplished over the input/output interface 430, for instance, where the computer system 400 is connected to a network and the programming and display operations occur on another associated computer, or via a detachable input device as is known with respect to interfacing programmable logic controllers.

Program storage memory 470 and data storage memory 480 can each comprise volatile (RAM) and non-volatile (ROM) memory units and can also comprise hard disk and backup storage capacity, and both program storage memory 470 and data storage memory 480 can be embodied in a single memory device or separated in plural memory devices. Program storage memory 470 stores software program modules and associated data and stores one or more of: a raw data receiving module 471, having one or more software programs adapted to receive the analytic characterization data 125, for instance obtained at step 120 in the process flow diagram of FIG. 1 ; an analytical value calculation module 472, having one or more software programs adapted to determine one or more analytical values 135 based on the type of analytic characterization data 125 received by module 471, for instance calculated at step 130 in the process flow diagram of FIG. 1 using equation (5) herein based on the FT-ICR mass spectroscopy data; one or more assigned assay value calculation modules 473, having one or more software programs adapted to determine a plurality of assigned assay values to produce a virtual assay 195 of an oil sample, for instance using the one or more analytical values 135 calculated by module 472 and the set of constants 105 (and in certain embodiments the density 110), for instance as in step 190 in the process flow diagram of FIG. 1 (in certain embodiments using steps 140, 150, 160, 170 and 180 to calculate and assign a plurality of different elemental composition values/physical properties/indicative properties that make up the virtual assay, for each of a total oil sample, a naphtha fraction, a gas oil fraction, a vacuum gas oil fraction and a vacuum residue fraction, respectively, to produce corresponding assigned assay values for the virtual assay 195, include including assigned assay values 145 pertaining to the total oil sample, assigned assay values 155 pertaining to a naphtha fraction, assigned assay values 165 pertaining to a gas oil fraction, assigned assay values 175 pertaining to a vacuum gas oil fraction and assigned assay values 185 pertaining to a vacuum residue fraction); and optionally a density receiving module 474 (in embodiments in which density is used to determine assigned assay values for the virtual assay), shown in dashed lines, having one or more software programs adapted to receive the density data 110, which in certain embodiments can be integrated in the raw data receiving module 471 or the assigned assay value calculation modules 473 (shown by overlapping dashed lines). Data storage memory 480 stores results and other data generated by the one or more program modules of the present invention, including the constants 105, the density 110, the analytic characterization data 125, the one or more analytical values 135, and the assigned assay values (which can be a single set of assigned data values to produce the virtual assay 195, or alternatively delineated by type including the assigned assay values 145, 155, 165, 175 and 185 described herein).

It is to be appreciated that the computer system 400 can be any computer such as a personal computer, minicomputer, workstation, mainframe, a dedicated controller such as a programmable logic controller, or a combination thereof. While the computer system 400 is shown, for illustration purposes, as a single computer unit, the system can comprise a group of computers which can be scaled depending on the processing load and database size.

Computer system 400 generally supports an operating system, for example stored in program storage memory 470 and executed by the processor 420 from volatile memory. According to an embodiment of the invention, the operating system contains instructions for interfacing computer system 400 to the Internet and/or to private networks.

Note that steps 110 and 120 can be carried out separate from or within the computer system 400. For example, step 110 can be carried out and the data entered into the computer system 400, for example via data storage memory 480, or as a single value incorporated in the program storage memory 470 for one or more of the modules. Step 120 can be carried out and the analytic characterization data entered into the computer system 400, for example via data storage memory 480, represented as the analytic characterization data 125.

In alternate embodiments, the present invention can be implemented as a computer program product for use with a computerized computing system. Those skilled in the art will readily appreciate that programs defining the functions of the present invention can be written in any appropriate programming language and delivered to a computer in any form, including but not limited to: (a) information permanently stored on non-writeable storage media (e.g., read-only memory devices such as ROMs or CD-ROM disks); (b) information alterably stored on writeable storage media (e.g., floppy disks and hard drives); and/or (c) information conveyed to a computer through communication media, such as a local area network, a telephone network, or a public network such as the Internet. When carrying computer readable instructions that implement the present invention methods, such computer readable media represent alternate embodiments of the present invention.

As generally illustrated herein, the system embodiments can incorporate a variety of computer readable media that comprise a computer usable medium having computer readable code means embodied therein. One skilled in the art will recognize that the software associated with the various processes described can be embodied in a wide variety of computer accessible media from which the software is loaded and activated. Pursuant to In re Beauregard, 35 U.S.P.Q.2d 1383 (U.S. Pat. No. 5,710,578), the present invention contemplates and includes this type of computer readable media within the scope of the invention. In certain embodiments, pursuant to In re Nuijten, 500 F.3d 1346 (Fed. Cir. 2007) (U.S. patent application Ser. No. 09/211,928), the scope of the present claims is limited to computer readable media, wherein the media is both tangible and non-transitory.

It is to be understood that like numerals in the drawings represent like elements through the several figures, and that not all components and/or steps described and illustrated with reference to the figures are required for all embodiments or arrangements. Further, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms ““including,” “comprising,” or “having,” “containing,” “involving,” and variations thereof herein, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should be noted that use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.

Notably, the figures and examples above are not meant to limit the scope of the present disclosure to a single implementation, as other implementations are possible by way of interchange of some or all the described or illustrated elements. Moreover, where certain elements of the present disclosure can be partially or fully implemented using known components, only those portions of such known components that are necessary for an understanding of the present disclosure are described, and detailed descriptions of other portions of such known components are omitted so as not to obscure the disclosure. In the present specification, an implementation showing a singular component should not necessarily be limited to other implementations including a plurality of the same component, and vice-versa, unless explicitly stated otherwise herein. Moreover, applicants do not intend for any term in the specification or claims to be ascribed an uncommon or special meaning unless explicitly set forth as such. Further, the present disclosure encompasses present and future known equivalents to the known components referred to herein by way of illustration.

The foregoing description of the specific implementations will so fully reveal the general nature of the disclosure that others can, by applying knowledge within the skill of the relevant art(s), readily modify and/or adapt for various applications such specific implementations, without undue experimentation, without departing from the general concept of the present disclosure. Such adaptations and modifications are therefore intended to be within the meaning and range of equivalents of the disclosed implementations, based on the teaching and guidance presented herein. It is to be understood that the phraseology or terminology herein is for the purpose of description and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by the skilled artisan in light of the teachings and guidance presented herein, in combination with the knowledge of one skilled in the relevant art(s). It is to be understood that dimensions discussed or shown are drawings are shown accordingly to one example and other dimensions can be used without departing from the disclosure.

The subject matter described above is provided by way of illustration only and should not be construed as limiting. Various modifications and changes can be made to the subject matter described herein without following the example embodiments and applications illustrated and described, and without departing from the true spirit and scope of the invention encompassed by the present disclosure, which is defined by the set of recitations in the following claims and by structures and functions or steps which are equivalent to these recitations.

TABLE 1 Fraction Boiling Point, ° C. Methane −161.5  Ethane −88.6 Propane −42.1 Butanes  −6.0 Light Naphtha 36-90 Mid Naphtha  90-160 Heavy Naphtha 160-205 Light gas Oil 205-260 Mid Gas Oil 260-315 Heavy gas Oil 315-370 Light Vacuum Gas Oil 370-430 Mid Vacuum Gas Oil 430-480 Heavy vacuum gas oil 480-565 Vacuum Residue 565+ 

TABLE 2 Property Property Unit Type Fraction Yield W % or V % Yield All API Gravity ° Physical All Kinematic Viscosity @ 38° C. cSt Physical Fraction boiling >250° C. Refractive Index @ 20° C. Unitless Physical Fraction boiling <400° C. Sulfur W % or ppmw Composition All Mercaptan Sulfur, W % W % Composition Fraction boiling <250° C. Nickel ppmw Composition Fraction boiling >400° C. Vanadium ppmw Composition Fraction boiling >400° C. Nitrogen ppmw Composition All Flash Point ° C. Indicative All Cloud Point ° C. Indicative Fraction boiling >250° C. Pour Point ° C. Indicative Fraction boiling >250° C. Freezing Point ° C. Indicative Fraction boiling >250° C. Micro Carbon Residue W % Indicative Fraction boiling >300° C. Smoke Point mm Indicative Fraction boiling between 150- 250° C. Octane Number Unitless Indicative Fraction boiling <250° C. Cetane Index Unitless Indicative Fraction boiling between 150- 400° C. Aniline Point ° C. Indicative Fraction boiling <520° C.

TABLE 3 Fraction property Units K_(AD) X1_(AD) Naphtha Aromatics (Aro) W % 2.765580E+01 −1.670937E+00 Hydrogen (H) W % 1.403537E+01  8.316279E−02 Paraffins (P) W % 9.502099E+01 −1.142910E+00 Sulfur (S) ppmw 9.800304E+03 −7.855451E+02 Octane Number (ON) Unitless 1.259002E+01  2.697909E+00 Gas Oil (GO) Aniline Point (AP) ° C. 4.770076E+01  3.597674E+00 Cetane Number (CN) Unitless 9.417837E+01 −1.102783E+01 Cloud Point (CP) ° C. 1.121797E+00 −1.183971E+00 Nitrogen (N) ppmw −1.982530E+02  −1.187859E+01 Sulfur (S) ppmw 1.045575E+04 −1.817641E+03 Kinematic Viscosity @40° C. cSt 4.745641E+00 −5.484670E−01 Pour Point (PP) ° C. 3.140376E+01 −9.522503E+00 Vacuum Gas Oil Nitrogen (N) ppmw −1.419580E+03   1.574851E+02 (VGO) Sulfur (S) ppmw 1.388546E+04  1.247189E+03 Vacuum Residue Micro Carbon Resid (MCR) W % −3.538348E+02   2.769305E+01 (VR) Sulfur (S) ppmw −1.794978E+04   5.332348E+03 Oil Sample C5-Asphaltenes (C5A) W % 2.481746E+00  1.449101E−01 Micro Carbon Resid (MCR) W % 1.015867E+01 −1.131874E+00 Pour Point (PP) ° C. −6.164055E+01   4.371275E+00 Kinematic Viscosity cSt −2.769687E+01  −5.887719E+00 @100° C. Kinematic Viscosity @70° C. cSt −1.445765E+02  −1.044097E+01 Nitrogen (N) ppmw −5.390376E+03   7.530139E+02 Sulfur (S) ppmw 2.153689E+04 −9.144543E+02 Total Acid Number (TAN) mg KOH/100 g −8.780766E−01   1.654558E−01 Aromatics (Aro) W % −1.750656E+01   3.269800E+00 Fraction property Units X2_(AD) X3_(AD) Naphtha Aromatics (Aro) W %  4.180934E−02 −2.660740E−04  Hydrogen (H) W % −2.216371E−03 1.411326E−05 Paraffins (P) W %  2.235499E−02 −1.451371E−04  Sulfur (S) ppmw  1.866435E+01 −1.167486E−01  Octane Number (ON) Unitless −5.581586E−02 3.343989E−04 Gas Oil (GO) Aniline Point (AP) ° C. −3.210932E−02 1.849997E−04 Cetane Number (CN) Unitless  8.883555E−02 −5.768642E−04  Cloud Point (CP) ° C.  3.249978E−02 −2.117705E−04  Nitrogen (N) ppmw −2.513598E−01 1.351340E−03 Sulfur (S) ppmw  5.082854E+00 −5.169292E−02  Kinematic Viscosity @40° C. cSt  4.918447E−03 −3.034831E−05  Pour Point (PP) ° C.  1.050822E−01 6.743050E−04 Vacuum Gas Oil Nitrogen (N) ppmw −3.543353E+00 2.219874E−02 (VGO) Sulfur (S) ppmw −2.717822E+01 1.361125E−01 Vacuum Residue Micro Carbon Resid (MCR) W % −6.084657E−01 3.664869E−03 (VR) Sulfur (S) ppmw  1.138608E+02 6.265090E−01 Oil Sample C5-Asphaltenes (C5A) W % −1.458560E−03 1.115490E−05 Micro Carbon Resid (MCR) W %  1.543506E−03 −9.110507E−06  Pour Point (PP) ° C. −4.002913E−02 1.968855E−04 Kinematic Viscosity cSt −9.057997E−02 5.723088E−04 @100° C. Kinematic Viscosity @70° C. cSt −3.269927E−01 2.026621E−03 Nitrogen (N) ppmw −1.076969E+01 6.468430E−02 Sulfur (S) ppmw −1.347486E+01 5.016525E−02 Total Acid Number (TAN) mg KOH/100 g −2.874081E−03 1.885112E−05 Aromatics (Aro) W % −3.402061E−02 1.700804E−04 Fraction property Units X4_(AD) Naphtha Aromatics (Aro) W % 0.000000E+00 Hydrogen (H) W % 0.000000E+00 Paraffins (P) W % 0.000000E+00 Sulfur (S) ppmw 0.000000E+00 Octane Number (ON) Unitless 0.000000E+00 Gas Oil (GO) Aniline Point (AP) ° C. −2.434125E+00  Cetane Number (CN) Unitless 8.513356E+00 Cloud Point (CP) ° C. −6.944011E−02  Nitrogen (N) ppmw 3.000766E+01 Sulfur (S) ppmw 1.994905E+03 Kinematic Viscosity @40° C. cSt 3.998962E−01 Pour Point (PP) ° C. 6.082604E+00 Vacuum Gas Oil Nitrogen (N) ppmw 0.000000E+00 (VGO) Sulfur (S) ppmw 0.000000E+00 Vacuum Residue Micro Carbon Resid (MCR) W % 0.000000E+00 (VR) Sulfur (S) ppmw 0.000000E+00 Oil Sample C5-Asphaltenes (C5A) W % −1.479317E−01  Micro Carbon Resid (MCR) W % 1.118189E+00 Pour Point (PP) ° C. −2.177504E+00  Kinematic Viscosity cSt 1.087127E+01 @100° C. Kinematic Viscosity @70° C. cSt 2.772947E+01 Nitrogen (N) ppmw −3.047269E+02  Sulfur (S) ppmw 1.798543E+03 Total Acid Number (TAN) mg KOH/100 g −6.247679E−02  Aromatics (Aro) W % −1.404593E+00 

TABLE 4 Double Bond Equivalent (DBE) Intensity 0 0 1 0 2 0 3 0 4 3047754803 5 4148548475 6 4106580447 7 4475073884 8 4874039296 9 4852787148 10 4060232629 11 2831278701 12 2726027390 13 2196336212 14 1348225844 15 980497462 16 604773496 17 455374155 18 0 19 0

TABLE 5 Conventional Calculated Crude Oil AD Value AD Description Unit Assay Value (Equation (4) Naphtha, Aro W % 11.0 11.0 Naphtha, H W % 14.7 14.7 Naphtha, P W % 75.8 75.8 Naphtha, S ppmw 876 876 Naphtha, ON Unitless 52.5 52.5 GO, AP ° C. 66.0 66.1 GO, CN Unitless 59.5 59.3 GO, CP ° C. −10.0 −10.4 GO, N ppmw 71.2 73.0 GO, S ppmw 13,090 13,424 GO, Kinematic Viscosity cSt 2.9 2.9 @40° C. GO, PP ° C. −9.0 −9.6 VGO, N ppmw 617 617 VGO, S ppmw 28,800 28,795 VR, MCR W % 12.4 12.4 VR, S ppmw 52,700 52,691 Oil Sample, C5A W % 1.4 1.4 Oil Sample, MCR W % 6.2 6.2 Oil Sample, PP ° C. −15.0 −17.9 Oil Sample, Kinematic cSt 11.8 11.8 Viscosity @100° C. Oil Sample, Kinematic cSt 21.7 21.8 Viscosity @70° C. Oil Sample, N ppmw 829 777 Oil Sample, S Ppmw 30,000 30,948 Oil Sample, TAN mg KOH/100 g 0.1 0.14 Oil Sample, Aro W % 20.2 20.0 

1. A method for producing a virtual assay of an oil sample, wherein the oil sample is characterized by a density, selected from the group consisting of crude oil, bitumen and shale oil, and characterized by naphtha, gas oil, vacuum gas oil and vacuum residue fractions, the method comprising: entering into a computer Fourier transform ion cyclotron resonance (FT-ICR) mass spectroscopy data indicative of intensities at corresponding double bond equivalent (DBE) values over a range of DBE values for a solution of the oil sample without distillation in a FT-ICR mass spectroscopy solvent; calculating and assigning, as a function of the FT-ICR mass spectroscopy data, an analytical value (AV); and calculating and assigning, as a function of the AV and the density of the oil sample, virtual assay data of the oil sample and the naphtha, gas oil, vacuum gas oil and vacuum residue fractions, said virtual assay data comprising a plurality of assigned data values.
 2. The method of claim 1, wherein virtual assay data comprises: a plurality of assigned assay data values pertaining to the oil sample including one or more of aromatic content, C5-asphaltenes content, elemental compositions of sulfur and nitrogen, micro-carbon residue content, total acid number and viscosity; a plurality of assigned assay values pertaining to the vacuum residue fraction of the oil sample including one or more of elemental composition of sulfur and micro-carbon residue content; a plurality of assigned assay values pertaining to the vacuum gas oil fraction of the oil sample including elemental compositions of one or more of sulfur and nitrogen; a plurality of assigned assay values pertaining to the gas oil fraction of the oil sample including one or more of elemental compositions of sulfur and nitrogen, viscosity, and indicative properties including aniline point, cetane number, cloud point and pour point; and a plurality of assigned assay values pertaining to the naphtha fraction of the oil sample including one or more of aromatic content, elemental composition of hydrogen and sulfur, paraffin content and octane number.
 3. The method of claim 1, wherein virtual assay data comprises: a plurality of assigned assay data values pertaining to the oil sample including aromatic content, C5-asphaltenes content, elemental compositions of sulfur and nitrogen, micro-carbon residue content, total acid number and viscosity; a plurality of assigned assay values pertaining to the vacuum residue fraction of the oil sample including elemental composition of sulfur and micro-carbon residue content; a plurality of assigned assay values pertaining to the vacuum gas oil fraction of the oil sample including elemental compositions of sulfur and nitrogen; a plurality of assigned assay values pertaining to the gas oil fraction of the oil sample including elemental compositions of sulfur and nitrogen, viscosity, and indicative properties including aniline point, cetane number, cloud point and pour point; and a plurality of assigned assay values pertaining to the naphtha fraction of the oil sample including aromatic content, elemental composition of hydrogen and sulfur, paraffin content and octane number.
 4. The method of claim 3, wherein virtual assay data further comprises: yields of fractions from the oil sample as mass fractions of boiling point ranges, including one or more of naphtha, gas oil, vacuum gas oil and vacuum residue; composition information of hydrogen sulfide and/or mercaptans in the oil sample and/or its fractions; elemental compositions of one or more of carbon, hydrogen, nickel, and vanadium; physical properties of the oil sample and/or its fractions including one or more of API gravity and refractive index; or indicative properties of the oil sample and/or its fractions including one or more of flash point, freezing point and smoke point.
 5. The method of claim 1, further comprising operating a FT-ICR mass spectrometer to obtain the FT-ICR mass spectroscopy data indicative of intensities at corresponding double bond equivalent (DBE) values over a range of DBE values, by analyzing the solution of the oil sample without distillation in the FT-ICR mass spectroscopy solvent.
 6. The method of claim 1, wherein each assay value is determined by a multi-variable polynomial equation with predetermined constant coefficients developed using linear regression techniques, wherein corresponding variables are the AV and the density of the oil sample.
 7. The method of claim 6, wherein each assay value is determined by AD=K _(AD) +X1_(AD) *AV+X2_(AD) *AV ² +X3_(AD) *AV ³ +X4_(AD) *ρ*AV where: AD is the assigned assay value that is a value and/or property representative of an elemental composition value, a physical property or an indicative property; AV is the analytical value of the oil sample; ρ is the density of the oil sample; and K_(AD), X1_(AD), X2_(AD), X3_(AD), and X4_(AD) are constants.
 8. The method of claim 6, wherein each assay value is determined by AD=K _(AD) +X1_(AD) *ρ+X2_(AD)*ρ² +X3_(AD)*ρ³ +X4_(AD) *AV+X5_(AD) *AV ² +X6_(AD) *AV ³ +X7_(AD) *ρ*AV where: AD is the assigned assay value that is a value and/or property representative of an elemental composition value, a physical property or an indicative property; AV is the analytical value of the oil sample; ρ is the density of the oil sample; and K_(AD), X1_(AD), X2_(AD), X3_(AD), X4_(AD), X5_(AD), X6_(AD) and X7_(AD) are constants.
 9. The method of claim 8, wherein the analytical value is an index derived from a summation of peak intensities over a range of DBE values.
 10. The method of claim 9, wherein the index is obtained by a function: ${FTMSI} = {\sum\limits_{{DBE} = \min}^{\max}{({Intensity})/\left( {{1E} + 9} \right)}}$ where: intensity=the intensity for each double bond equivalent (DBE) over a range of values min to max, and FTMSI is the index.
 11. A system for producing a virtual assay of an oil sample, wherein the oil sample is characterized by a density, selected from the group consisting of crude oil, bitumen and shale oil, and characterized by naphtha, gas oil, vacuum gas oil and vacuum residue fractions, the system comprising: a FT-ICR mass spectrometer that outputs FT-ICR mass spectroscopy data; a non-volatile memory device that stores calculation modules and data, the data including the FT-ICR mass spectroscopy data, wherein the FT-ICR mass spectroscopy data is indicative of intensities at corresponding double bond equivalent (DBE) values over a range of DBE values for a solution of the oil sample without distillation in a FT-ICR mass spectroscopy solvent; a processor coupled to the non-volatile memory device; a first calculation module that is stored in the non-volatile memory device and that is executed by the processor, wherein the first calculation module calculates an analytical value (AV) as a function of the FT-ICR mass spectroscopy data; and a second calculation module that is stored in the non-volatile memory device and that is executed by the processor, wherein the second calculation module calculates, as a function of the AV and the density of the oil sample, virtual assay data of the oil sample and the naphtha, gas oil, vacuum gas oil and vacuum residue fractions, said virtual assay data comprising a plurality of assigned data values.
 12. The system as in claim 11, wherein virtual assay data comprises: a plurality of assigned assay data values pertaining to the oil sample including aromatic content, C5-asphaltenes content, elemental compositions of sulfur and nitrogen, micro-carbon residue content, total acid number and viscosity; a plurality of assigned assay values pertaining to the vacuum residue fraction of the oil sample including elemental composition of sulfur and micro-carbon residue content; a plurality of assigned assay values pertaining to the vacuum gas oil fraction of the oil sample including elemental compositions of sulfur and nitrogen; a plurality of assigned assay values pertaining to the gas oil fraction of the oil sample including elemental compositions of sulfur and nitrogen, viscosity, and indicative properties including aniline point, cetane number, cloud point and pour point; a plurality of assigned assay values pertaining to the naphtha fraction of the oil sample including aromatic content, elemental composition of hydrogen and sulfur, paraffin content and octane number.
 13. The system as in claim 12, wherein virtual assay data further comprises: yields of fractions from the oil sample as mass fractions of boiling point ranges, including one or more of naphtha, gas oil, vacuum gas oil and vacuum residue; composition information of hydrogen sulfide and/or mercaptans in the oil sample and/or its fractions; elemental compositions of one or more of carbon, hydrogen, nickel, and vanadium; physical properties of the oil sample and/or its fractions including one or more of API gravity and refractive index; or indicative properties of the oil sample and/or its fractions including one or more of flash point, freezing point and smoke point.
 14. The system of claim 11, wherein each assay value is calculated and assigned by the second calculation module with a multi-variable polynomial equation with predetermined constant coefficients developed using linear regression techniques, wherein corresponding variables are the AV and the density of the oil sample.
 15. The system of claim 14, wherein each assay value is calculated and assigned by the second calculation module with a function: AD=K _(AD) +X1_(AD) *AV+X2_(AD) *AV ² +X3_(AD) *AV ³ +X4_(AD) *ρ*AV where: AD is the assigned assay value that is a value and/or property representative of an elemental composition value, a physical property or an indicative property; AV is the analytical value of the oil sample; ρ is the density of the oil sample; and K_(AD), X1_(AD), X2_(AD), X3_(AD), and X4_(AD) are constants.
 16. The system of claim 14, wherein each assay value is calculated and assigned by the second calculation module with a function: AD=K _(AD) +X1_(AD) *ρ+X2_(AD)*ρ² +X3_(AD)*ρ³ +X4_(AD) *AV+X5_(AD) *AV ² +X6_(AD) *AV ³ +X7_(AD) *ρ*AV where: AD is the assigned assay value that is a value and/or property representative of an elemental composition value, a physical property or an indicative property; AV is the analytical value of the oil sample; ρ is the density of the oil sample; and K_(AD), X1_(AD), X2_(AD), X3_(AD), X4_(AD), X5_(AD), X6_(AD) and X7_(AD) are constants.
 17. The system of claim 16, wherein the analytical value is an index derived from a summation of peak intensities over a range of DBE values.
 18. The system of claim 17, wherein ${FTMSI} = {\sum\limits_{{DBE} = \min}^{\max}{({Intensity})/\left( {{1E} + 9} \right)}}$ where: intensity=the intensity for each double bond equivalent (DBE) over a range of values min to max, and FTMSI is the index. 